Inventory control, revenue management and pricing: Data-driven stochastic optimization approaches

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Background. Supply chain management plays an important role in the Canadian and global economy, and matching supply with demand is becoming increasingly more difficult with system complexity and product proliferation. With the rise of information systems, firms can have more visibility into supply networks as well as customer decision-making. While this emerging data holds the potential to revolutionize supply chain management, it also poses new challenges due to its size, type and velocity, requiring firms to adopt new ways to obtain insight and to control supply-demand processes. Goal and Objective. Our research goal is to contribute to the overall performance of supply chains through business analytics and data science. We will propose and analyze models that capture essential trade-offs in supply systems, and develop data-driven methods. The emphasis is given to incorporating data more directly into the decision making process. More specifically, (a) we will develop data-driven methods for inventory systems, handling issues such as censored demand information, multiple supply layers, perishability, and risk profiles. (b) We will also study customer behaviour, both for stationary products and fast-transitioning products, to incorporate emerging choice models into the firm's pricing and assortment decisions, striking a balance between learning and earning (exploration and exploitation). Furthermore, (c) we will examine how the principles of data-driven supply chain management can be applied to Canada's health care and transportation industries. Scientific Approach. We will use three aspects of analytics. (a) Descriptive analytics: Observation and interviews with industry partners; descriptive statistics; statistical inference. (b) Predictive analytics: regression; simulation; classification trees; clustering; machine learning. (c) Prescriptive analytics: stochastic optimization; dynamic programming; heuristic methods. Anticipated Outcome. We will disseminate our findings through publications, academic conferences and practitioner-oriented industry councils. Doctoral students will be trained as competent scholars in researching supply chains, and other students will champion data analytics in the private sector or the government. (HQPs account for a majority of the budget.) Benefit to Canada and the Marketplace. Data-driven supply chain management is becoming integral to operational competency and competitiveness. Through better use of data, product and service providers will better manage processes, resulting in higher profitability and competitiveness. Firms that do not excel in business analytics will lag behind. End-customers will enjoy better product offerings and service experiences. Government policy makers will gain insight into the impact of regulatory alternatives as one-tenth of Canada's gross domestic product is tied to supply chain activities.